1
Iris Identification Using
Wavelet Packets
Emine Krichen, Mohamed Anouar Mellakh, Sonia
Garcia Salicetti, Bernadette Dorizzi
{emine.krichen,anouar
-
mellakh;sonia.salicetti;bernadette.dorizzi}@int
-
evry.fr
Institut National des Télécommunications
9 Rue Charles Fourier , 91011 Evry France
2
Outline
•
Classical
approach
versus
our
approach
(Packets
Method)
•
Experimentations
on
2
databases
•
Introduction
of
color
information
•
Conclusion
and
perspectives
3
Introduction
•
Study of
iris recognition on normal
light illumination
–
Use
of
usual devices
–
Fusion
between
iris
and
other
biometric
modalities
(face,
eye
shape
…
)
4
Comparison infra
-
red / normal light
Normal light Near Infra red
•
Lack of texture information
•
Presence of a great number of reflections
5
Iris Segmentation
Hough Transform (Iris circle)
Circular Edge detector
6
Wavelet method
•
2D wavelet basis : Gabor
•
Spatial parameters in
polar coordinates (
ρ
,
θ
).
•
4
resolution
levels
•
2048 coefficients for
coding the iris.
d
φ
d
ρ
ρ
φ
ρ,
I
e
e
e
2
2
0
2
2
0
0
β
φ
θ
α
ρ
r
φ
θ
i
ω
J. Daugman, “How iris recognition works”, Proceedings of the International
Conference on Image Processing, 22
-
25 September 2002
7
Our approach : Packet method
•
Process the whole
image
at each level
of resolution
•
Starting with higher
mother wavelet
window
•
1664 coefficients for
coding iris
8
Databases
•
IrisINT
:
Iris
images
recorded
under
normal
light
illumination
.
70
persons
700
images
.
•
CASIA
:
Iris
images
taken
under
infra
red
illumination
.
110
persons,
770
images
.
Recorded
at
NLPR
China
.
9
Roc curves (IrisINT)
•
Poor results for the wavelet method
•
The wavelet Packet method is more
robust using visible light images
10
Comparative results on CASIA
and IrisINT
Databases
IrisINT
CASIA
Type of errors
FAR
FRR
FAR
FRR
Classical wavelet method
2%
12.04%
0.35%
2.08%
Packets method
0%
0.57%
0.2%
1.38%
•
With
infra
red
illumination,
the
two
methods
have
quite
the
same
performance
.
WP
is
more
robust
to
the
presence
of
eyelids
or
eyelashes
.
C.P. Strouthopoulos, Adaptive
color reduction
11
Use of color information
ACR method
Original color image
(71.000 different colors)
Color image (256 colors)
We
perform
iris
recognition
using
the
same
algorithm
as
the
one
developed
for
grey
level
image
12
Use of color information :
ROC curve on IrisINT
Use of color information allows a better
discrimination between the persons.
13
Conclusion and perspectives
•
The
packets
method
allows
better
performance
on
normal
light
illumination
images
.
•
Color
information
can
be
used
to
improve
results
on
simple
grey
level
images
.
•
Results
need
to
be
confirmed
using
larger
bimodal
database
(in
order
to
decrease
the
variance)
.
14
Adaptive color reduction (ACR)
Self organized neural network
Reduction adapted to initial distribution of colors
N
.
Papamarkos,
A
.
E
.
Atsalakis,
and
C
.
P
.
Strouthopoulos,
Adaptive
colour
reduction,
IEEE
Transactions
on
Systems,
Man,
and
Cybernetics
,
Vol
.
32
,
N
°
1
,
,
February
2002
.
RGB +
neighborhood
information
One
Neuron
per color
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